What are the most common data quality issues that undermine AI performance?
In this clip from the “The Hidden Costs of Poor Data Quality in AI” panel hosted by Data Science Connect, Jon Malloy, Senior Technical Account Manager at Snowplow, shares the #1 issue he encounters in real-world organizations: data you think you have, but actually don’t.
Jon explains:
- How missing or incomplete data silently corrupts machine learning outputs
- Why opt-outs, tracking gaps, and unobserved users create misleading “representative” samples
- How organizations mistakenly assume their AI models reflect the entire user base
- Why understanding your data sources, collection flow, and full ecosystem is essential
- How to avoid “unknown unknowns” by mapping the domain of what your data can and cannot support
This insight is crucial for anyone working in AI, machine learning, MLOps, data engineering, analytics, event data, and data governance.
🔗 Watch the full webinar here:
The Hidden Costs of Poor Data Quality in AI
https://snowplow.io/events/the-hidden-costs-of-poor-data-quality-in-ai
#eventdata #snowplow #dataquality
missing data, AI performance issues, machine learning bias, data quality risks, Snowplow, event data collection, ML models, data science, Data Science Connect.